Fault diagnosis of wind turbine gearbox based on wavelet packet denoising and CNN-Swin Transformer-LSTM

The working environment of wind turbine gearboxes is complex and variable, with strong noise, which makes traditional fault diagnosis methods inadequate for accurate fault identification. To address this issue, this paper proposes a fault diagnosis method based on Wavelet Packet Denoising combined with CNN-Swin Transformer-LSTM. Firstly, the original signal is decomposed, denoised, and reconstructed using wavelet packets to highlight the effective periodic impact components within the signal, and the reconstructed signal is converted into two-dimensional wavelet timefrequency images. Then, Convolutional Neural Networks (CNN) are used to extract basic feature information from the images. The feature maps are then input into a Swin Transformer model to automatically extract multi-scale feature information based on the self-attention mechanism. Following this, Long Short-Term Memory (LSTM) networks are employed to capture temporal features of the data. Additionally, the Convolutional Block Attention Module (CBAM) is introduced to enhance feature representation capability. Finally, the method classifies different fault types. Experimental verification shows that the proposed method achieves an accuracy of 99.62% and 99.46% on two working condition datasets, respectively. Under conditions of strong noise and variable working conditions, the fault diagnosis accuracy reaches 92.24% and 96.16%. The experimental results demonstrate that this model possesses strong feature learning capabilities, robust anti-interference ability, and good generalization performance. Compared to other existing diagnosis techniques, it exhibits superior diagnostic performance and reliability.